Baseline ECG Outperforms the Angiogram for Predicting Mortality in COVID-19–Associated STEMI: Insights From the NACMI Registry
Bibliographic record
Abstract
Background Patients with COVID-19 continue to present with ST-elevation myocardial infarction (STEMI). To date, there have been no core laboratory electrocardiogram (ECG) description of these patients. Accordingly, we aimed to evaluate the ECG characteristics and coronary angiograms of patients hospitalized with COVID-19–associated STEMI. Methods In a prespecified analysis from the North American COVID-19 Myocardial Infarction (NACMI) registry, we collected baseline STEMI ECGs in COVID-19–positive patients for core laboratory interpretation (Canadian VIGOUR Centre, Edmonton, Canada), including ST-segment analysis, worst lead ST-elevation (ST-E), sum ST-elevation (ΣST-E), and sum ST deviation (ΣST-D). Available core laboratory angiograms (Cardiovascular Imaging Research Core Laboratory, Vancouver, Canada) were also collected. ECG and angiographic variables associated with in-hospital death were analyzed. Results Of the 392 patients, 28.1% died in-hospital. Greater median ST-E (deviation) was observed in those who died (ST-E: 2.20 vs 1.65 mm; P = .006; ΣST-E: 7 vs 5 mm; P = .005; ΣST-D: 11 vs 9 mm; P = .013, respectively). Following adjustment for clinical characteristics, ΣST-D was a positive predictor of in-hospital death (relative risk [RR], 1.03; 95% CI, 1.01-1.05; P = .003). In 173 patients with available coronary angiograms, 32% of the baseline ECGs could not define infarct location (19% with angiography). Following adjustment, only ECG characteristics were independent predictors of in-hospital mortality (ST-E: RR, 1.14; 95% CI, 1.04-1.22; ΣST-E: RR, 1.03; 95% CI. 1.01-1.05: ΣST-D: RR, 1.03; 95% CI, 1.01-1.05). Conclusions In COVID-19–positive patients with STEMI, ST-E (deviation) is a predictor of in-hospital mortality and likely reflects microthrombi in multiple territories compromising myocardial perfusion. Our data support ECG as a simple tool to help risk stratify patients with COVID-19–associated STEMI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".